1. Field of Invention
The present invention pertains to the field of genetic programming. More particularly, this invention relates to genetic data representations in genetic programming.
2. Art Background
Genetic programming may be used to obtain a variety of problem solutions. A problem solution obtainable through genetic programming may take the form of a computer program, a math function, an electrical circuit, finite automata, a graph structure, or a neural network to name a few examples.
Genetic programming may be defined as a computer-based programming methodology in which problem solutions are generated using an iterative process that simulates evolution by natural selection. Genetic programming typically involves the generation of an initial population of candidate solutions. A candidate solution plays a role analogous to an organism in biological evolution. Each candidate solution in a population is typically evaluated as a solution to a particular development problem using a fitness measure. If a candidate solution is considered good enough in terms of the fitness measure, then it is usually selected as the solution. Otherwise, a subset of the candidate solutions from the population are typically selected to become parents for a population of child candidate solutions. The child candidate solutions are then generated and evaluated as solutions using the fitness measure. The process repeats through generations of child populations until an individual candidate solution that is good enough is found or until it is decided that the process has gone on sufficiently long that it is not worth proceeding.
Child candidate solutions are typically created by combining genetic data components from parent candidate solutions using techniques that are modeled on biological processes such as mutation and crossover. Typically, the genetic data component of a candidate solution in prior genetic programming methods is represented as a parse tree or a sequence of instructions. It is desirable to use a genetic data component representation that decreases the number of generations of candidate solutions that need to be evaluated before obtaining a suitable solution. This would decrease the overall costs associated with using genetic programming to obtain problem solutions.